Autonomous Corrosion Assessment of Reinforced Concrete Structures: Feasibility Study

Taffese, Woubishet Zewdu; Nigussie, Ethiopia

doi:10.3390/s20236825

Cited by 17 publications

(2 citation statements)

References 124 publications

(153 reference statements)

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“…Wu et al (2019b) have incorporated deep learning algorithms into edge devices for assessment and damage detection to achieve quick inference and low memory demands through transfer learning and network pruning. Another research has studied the technological feasibility of autonomous corrosion assessment of reinforced concrete structures, in which the use of IoT and machine learning for autonomous corrosion condition assessment of RC structures were recommended (Taffese and Nigussie, 2020). Maraveas and Bartzanas (2021) have studied another type of structure through using various sensors for accurate and real-time monitoring of agricultural building structures, including electrochemical, ultrasonic, fiber-optic, CI 22,3 piezoelectric, wireless, fiber Bragg grating sensors and self-sensing concrete.…”

Section: Deep Leaning and Internet Of Things To Deliver Smart Cities ...mentioning

confidence: 99%

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

Elghaish

Matarneh

Alhusban

2021

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Purpose The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the current state of using deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions, as well as finding research gaps to carry out more research to bridge revealed knowledge and practice gaps. Design/methodology/approach The scientometric analysis is conducted for 181 articles to assess the density of publications in different topics of deep learning-based construction management applications. After that, a thematic and gap analysis are conducted to analyze contributions and limitations of key published articles in each area of application. Findings The scientometric analysis indicates that there are four main applications of deep learning in construction management, namely, automating progress monitoring, automating safety warning for workers, managing construction equipment, integrating Internet of things with deep learning to automatically collect data from the site. The thematic and gap analysis refers to many successful cases of using deep learning in automating site management tasks; however, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners and workers perspectives to implement existing applications in their daily tasks. Practical implications This paper enables researchers to directly find the research gaps in the existing solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected on speeding the digital construction transformation, which is a strategy over the world. Originality/value To the best of the authors’ knowledge, this paper is the first of its kind to adopt a structured technique to assess deep learning-based construction site management applications to enable researcher/practitioners to either adopting these applications in their projects or conducting further research to extend existing solutions and bridging revealed knowledge gaps.

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Section: Deep Leaning and Internet Of Things To Deliver Smart Cities ...mentioning

confidence: 99%

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

Elghaish

Matarneh

Alhusban

2021

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“…However, their result interpretation is not easy because minor changes in humidity or temperature significantly affect the taken measurements. Besides, these sensors' stability has been studied only for relatively short periods [67].…”

Section: Introductionmentioning

confidence: 99%

Corrosion Assessment in Reinforced Concrete Structures by Means of Embedded Sensors and Multivariate Analysis—Part 1: Laboratory Validation

Ramón-Zamora,

Lliso-Ferrando,

Martínez-Ibernón

et al. 2023

Sensors

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Reinforced Concrete Structures (RCS) are a fundamental part of a country’s civil infrastructure. However, RCSs are often affected by rebar corrosion, which poses a major problem because it reduces their service life. The traditionally used inspection and management methods applied to RCSs are poorly operative. Structural Health Monitoring and Management (SHMM) by means of embedded sensors to analyse corrosion in RCSs is an emerging alternative, but one that still involves different challenges. Examples of SHMM include INESSCOM (Integrated Sensor Network for Smart Corrosion Monitoring), a tool that has already been implemented in different real-life cases. Nevertheless, work continues to upgrade it. To do so, the authors of this work consider implementing a new measurement procedure to identify the triggering agent of the corrosion process by analysing the double-layer capacitance of the sensors’ responses. This study was carried out on reinforced concrete specimens exposed for 18 months to different atmospheres. The results demonstrate the proposed measurement protocol and the multivariate analysis can differentiate the factor that triggers corrosion (chlorides or carbonation), even when the corrosion kinetics are similar. Data were validated by principal component analysis (PCA) and by the visual inspection of samples and rebars at the end of the study.

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Deep Learning to Improve Construction Site Management Tasks

Elghaish

Rahimian

Brooks

et al. 2022

Blockchain of Things and Deep Learning Applications in Construction

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Autonomous Corrosion Assessment of Reinforced Concrete Structures: Feasibility Study

Cited by 17 publications

References 124 publications

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

Corrosion Assessment in Reinforced Concrete Structures by Means of Embedded Sensors and Multivariate Analysis—Part 1: Laboratory Validation

Deep Learning to Improve Construction Site Management Tasks

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